Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations; sorting the first data based upon incident categories; applying seasonal trend decomposition with loess smoothing for the first data; plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit; displaying the plotted incidents to a user via a graphic user interface; obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations; filtering the first data based upon the correlation of the first data with the second data; and predicting the occurrence of additional incidents based upon the filtered data.
The system visually analyzes crime, traffic, and civil incident data. It obtains incident reports from various locations, sorts them by category, and applies seasonal trend decomposition with loess smoothing to identify patterns. The system then plots incidents on a map as points, heatmaps, or choropleth maps for specific time periods and geographic areas, displaying them in a user interface. It also obtains demographic, weather, or special events data and filters incident data based on its correlation with this second dataset. Finally, the system predicts future incidents based on the filtered data.
2. A method comprising: obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations; sorting the first data based upon incident categories; applying seasonal trend decomposition with loess smoothing for the first data; plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit; and displaying the plotted incidents to a user via a graphic user interface, wherein plotting the incidents within the first data is performed as a density estimated heatmap, wherein the density estimated heatmap is performed by employing a variable kernel method which scales the parameter of an estimation by allowing a kernel width to vary according to the algorithm f ( x ) = 1 N + ∑ i = 1 N 1 max ( h , d i , k ) K ( x - X i max ( h , d i , k ) ) .
The system visually analyzes crime, traffic, and civil incident data. It obtains incident reports from various locations, sorts them by category, and applies seasonal trend decomposition with loess smoothing to identify patterns. The system plots incidents on a map as points, heatmaps, or choropleth maps for specific time periods and geographic areas, displaying them in a user interface. Specifically, it uses a density-estimated heatmap, employing a variable kernel method to scale the estimation parameter. This allows the kernel width to vary according to the algorithm: f(x) = (1 / N) + Σ(i=1 to N) [1 / max(h, di, k)] * K[(x - Xi) / max(h, di, k)], where 'h', 'di', and 'k' are parameters influencing kernel width.
3. The method of claim 2 , further comprising generating display information based on the density estimated heatmap.
The system, which analyzes crime data using heatmaps as described in claim 2, then generates display information based on the calculated density-estimated heatmap. This step determines how the heatmap is visually presented to the user, including color gradients, intensity levels, and other visual cues to highlight areas with high incident density.
4. The method of claim 1 , further comprising plotting spatial statistics of the first data utilizing a multidirectional optimum ectope-based algorithm to create a spatial weights matrix to identify hot and cold spots in mapped data.
The system, which analyzes crime data as described in claim 1, plots spatial statistics of the incident data using a multidirectional optimum ectope-based algorithm. This algorithm creates a spatial weights matrix to identify statistically significant hot spots (areas with high incident concentration) and cold spots (areas with low incident concentration) in the mapped data, aiding in identifying areas requiring focused attention.
5. The method of claim 1 , further comprising: generating quantile measures based on a select epicenter and time window; displaying information representative of the quantile measures.
The system, which analyzes crime data as described in claim 1, generates quantile measures based on a selected epicenter (location) and a defined time window. The system displays information representative of these quantile measures, showing the distribution of incidents around the epicenter within the specified time frame.
6. The method of claim 2 , further comprising obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations.
The system, which analyzes crime data using heatmaps as described in claim 2, obtains demographic data, weather data, or special events data. This second dataset contains information related to various locations and is used in conjunction with incident reports to identify correlations and potential contributing factors.
7. The method of claim 6 , further comprising: filtering the first data based upon the correlation of the first data with the second data.
The system, which analyzes crime data and obtains demographic data as described in claim 6, filters the incident data based on its correlation with the demographic, weather, or special events data. This filtering step helps identify relationships between environmental factors and incident occurrences, allowing for more targeted analysis and prediction.
8. The method of claim 7 , further comprising predicting the occurrence of additional incidents based upon the filtered data.
The system, which analyzes crime data, obtains demographic data, and filters data as described in claim 7, predicts the occurrence of additional incidents based on the filtered data. By identifying correlations between environmental factors and incident patterns, the system can forecast future crime hotspots or emerging trends.
9. A method comprising: obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations; sorting the first data based upon incident categories; applying seasonal trend decomposition with loess smoothing only for the first data; plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit; obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations; and displaying the plotted incidents and second data to a user via a graphic user interface.
The system visually analyzes crime, traffic, and civil incident data. It obtains incident reports from various locations, sorts them by category, and applies seasonal trend decomposition with loess smoothing to identify patterns. The system then plots incidents on a map as points, heatmaps, or choropleth maps for specific time periods and geographic areas. It also obtains demographic, weather, or special events data, and displays both the plotted incidents and this second dataset to a user via a graphic user interface.
10. The method of claim 9 , further comprising: generating quantile measures based on a select epicenter and time window; displaying information representative of the quantile measures.
The system, which analyzes crime data and presents related demographic information as described in claim 9, generates quantile measures based on a selected epicenter (location) and a defined time window. The system then displays information representative of these quantile measures, illustrating the distribution of incidents around the epicenter within the specified timeframe.
11. A method comprising: obtaining first data regarding criminal, traffic and/or civil incidents, the first data comprising reported criminal, traffic and/or civil incidents from a plurality of locations; sorting the first data based upon incident categories; applying seasonal trend decomposition with loess smoothing for the first data; plotting incidents within the first data as points, density estimated heatmaps, or chloropleth maps for a defined geotemporal unit; obtaining second data regarding demographic data, weather data, or special events data, the second data comprising data related to a plurality of locations; and displaying the plotted incidents and second data to a user via a graphic user interface, wherein plotting the incidents within the first data is performed as a density estimated heatmap, wherein the density estimated heatmap is performed by employing a variable kernel method which scales the parameter of an estimation by allowing a kernel width to vary according to the algorithm f ( x ) = 1 N + ∑ i = 1 N 1 max ( h , d i , k ) K ( x - X i max ( h , d i , k ) ) .
The system visually analyzes crime, traffic, and civil incident data. It obtains incident reports from various locations, sorts them by category, and applies seasonal trend decomposition with loess smoothing to identify patterns. The system plots incidents on a map as points, heatmaps, or choropleth maps for specific time periods and geographic areas. It also obtains demographic, weather, or special events data, and displays both the plotted incidents and this second dataset to a user via a graphic user interface. Specifically, it uses a density-estimated heatmap, employing a variable kernel method to scale the estimation parameter, according to the algorithm: f(x) = (1 / N) + Σ(i=1 to N) [1 / max(h, di, k)] * K[(x - Xi) / max(h, di, k)].
12. The method of claim 11 , wherein generating the heatmap includes displaying a color of the glyph based on the value for the site.
The system that analyzes crime data using heatmaps as described in claim 11 includes generating the heatmap and displaying a color of the glyph based on the value for the site. This means the color displayed for each location on the heatmap corresponds to the incident density, providing a clear visual representation of high and low incident areas.
13. The method of claim 9 , further comprising plotting spatial statistics of the first data utilizing a multidirectional optimum ectope-based algorithm to create a spatial weights matrix to identify hot and cold spots in mapped data.
The system, which analyzes crime data and presents related demographic information as described in claim 9, plots spatial statistics of the incident data using a multidirectional optimum ectope-based algorithm. This algorithm creates a spatial weights matrix to identify statistically significant hot spots (areas with high incident concentration) and cold spots (areas with low incident concentration) in the mapped data, aiding in identifying areas requiring focused attention.
14. The method of claim 13 , wherein the spatial weights matrix is determined according to the value of G* i , where G* i is defined as: G i * = ∑ j = 1 N w ij x j - x _ ∑ j = 1 N w ij S [ ∑ n j = 1 w if 2 - ( ∑ n j = 1 w ij 2 ) 2 ] N - 1 .
The system, that analyzes crime data using spatial statistics as described in claim 13, determines the spatial weights matrix according to the value of G*i. G*i is defined as: G*i = [Σ(j=1 to N) (wij * xj) - (x_ * Σ(j=1 to N) wij)] / {S * [ (N * Σ(j=1 to N) (wij^2)) - (Σ(j=1 to N) wij)^2] / (N - 1) }^0.5. This formula is used to calculate the spatial autocorrelation of incidents, highlighting areas where similar values cluster together.
15. The method of claim 14 , further comprising: displaying a color of a group based on the G* i value.
The system that analyzes crime data using spatial statistics to create a spatial weights matrix and calculates G*i values as described in claim 14, displays a color of a group (area on the map) based on the G*i value. This means that areas with high positive G*i values (hot spots) are represented with one color, and areas with low negative G*i values (cold spots) are represented with another color, providing visual insight into statistically significant clusters.
Unknown
September 30, 2014
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